{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:41:06Z","timestamp":1776444066921,"version":"3.51.2"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.<\/jats:p>","DOI":"10.3390\/rs11111307","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T02:08:40Z","timestamp":1559527720000},"page":"1307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":298,"title":["Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Wenping","family":"Ma","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Qifan","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3459-5079","authenticated-orcid":false,"given":"Yue","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-2042","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2014.2325970","article-title":"A novel point-matching algorithm based on fast sample consensus for image registration","volume":"12","author":"Wu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/LGRS.2017.2783879","article-title":"PSOSAC: particle swarm optimization sample consensus algorithm for remote sensing image registration","volume":"15","author":"Wu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ma, W., Zhang, J., Wu, Y., Jiao, L., Zhu, H., and Zhao, W. (2019). A Novel Two-Step Registration Method for Remote Sensing Images Based on Deep and Local Features. IEEE Trans. Geosc. Remote Sens.","DOI":"10.1109\/TGRS.2019.2893310"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ma, W., Xiong, Y., Wu, Y., Yang, H., Zhang, X., and Jiao, L. (2019). Change Detection in Remote Sensing Images Based on Image Mapping and a Deep Capsule Network. Remote Sens., 11.","DOI":"10.3390\/rs11060626"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, H., Wu, Y., Xiong, Y., Hu, T., Jiao, L., and Hou, B. (2019). Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images. Remote Sens., 11.","DOI":"10.3390\/rs11020142"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ma, W., Guo, Q., Wu, Y., Zhao, W., Zhang, X., and Jiao, L. (2019). A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11070737"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Z., Huang, L., and He, J. (2019). A Multiscale Deep Middle-level Feature Fusion Network for Hyperspectral Classification. Remote Sens., 11.","DOI":"10.3390\/rs11060695"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"725","DOI":"10.14358\/PERS.80.8.725","article-title":"Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+","volume":"80","author":"Awad","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liang, H., and Li, Q. (2016). Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sens., 8.","DOI":"10.3390\/rs8020099"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2017.2686842","article-title":"A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification","volume":"55","author":"Sun","year":"2017","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Marinelli, D., Bovolo, F., and Bruzzone, L. (2019). A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors. IEEE Trans. Geosc. Remote Sens.","DOI":"10.1109\/TGRS.2019.2894339"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.3390\/rs70403966","article-title":"Global and local real-time anomaly detectors for hyperspectral remote sensing imagery","volume":"7","author":"Zhao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ecoinf.2014.07.004","article-title":"Sea water chlorophyll-a estimation using hyperspectral images and supervised artificial neural network","volume":"24","author":"Awad","year":"2014","journal-title":"Ecol. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-filtering-based nearest regularized subspace for hyperspectral image classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and feature extraction for remote sensing images from urban areas based on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geoscie. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sidike, P., Chen, C., Asari, V., Xu, Y., and Li, W. (2016, January 21\u201324). Classification of hyperspectral image using multiscale spatial texture features. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071767"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised spectral\u2013spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.1109\/JSTARS.2016.2517204","article-title":"Spectral\u2013spatial classification of hyperspectral image based on deep auto-encoder","volume":"9","author":"Ma","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/LGRS.2017.2737823","article-title":"Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2018.09.007","article-title":"Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis","volume":"146","author":"Sidike","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hu, W., Huang, Y., Wei, L., Zhang, F., and Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. J. Sens., 2015.","DOI":"10.1155\/2015\/258619"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral-spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TGRS.2018.2838665","article-title":"Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TNNLS.2018.2841009","article-title":"Self-paced learning-based probability subspace projection for hyperspectral image classification","volume":"30","author":"Yang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.isprsjprs.2015.03.006","article-title":"A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination","volume":"105","author":"Tan","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Zhang, M., Gong, M., Mao, Y., Li, J., and Wu, Y. (2018). Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.patcog.2017.09.007","article-title":"Superpixel-based 3D deep neural networks for hyperspectral image classification","volume":"74","author":"Shi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","article-title":"SuperPCA: A superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery","volume":"56","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/TGRS.2018.2861992","article-title":"Hyperspectral image classification in the presence of noisy labels","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A Fast Dense Spectral\u2013Spatial Convolution Network Framework for Hyperspectral Images Classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J.C.W. (2019). Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and So Kweon, I. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","unstructured":"Mnih, V., Heess, N., and Graves, A. (2014). Recurrent models of visual attention. Adv. Neural Inf. Process. Syst., 2204\u20132212."},{"key":"ref_40","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 6\u201311). Show, attend and tell: Neural image caption generation with visual attention. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_41","unstructured":"Zhu, Y., Groth, O., Bernstein, M., and Fei-Fei, L. (July, January 26). Visual7w: Grounded question answering in images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_42","unstructured":"Yang, Z., He, X., Gao, J., Deng, L., and Smola, A. (July, January 26). Stacked attention networks for image question answering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nam, H., Ha, J.W., and Kim, J. (2017, January 21\u201326). Dual attention networks for multimodal reasoning and matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.232"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1109\/TGRS.2010.2046494","article-title":"Hyperspectral region classification using a three-dimensional Gabor filterbank","volume":"48","author":"Bau","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:18Z","timestamp":1760187318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,1]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111307"],"URL":"https:\/\/doi.org\/10.3390\/rs11111307","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,1]]}}}